UCSF

Prostate cancer progression after radical prostatectomy poses a significant risk to patient health. The ability to predict which patients are at a higher risk of progression is crucial for determining appropriate adjuvant therapies. This study investigates the application of convolutional neural networks (CNNs) to pre-surgical multiparametric MRI (mpMRI) for predicting post-surgical prostate cancer progression. The study utilizes a retrospective patient cohort and explores the performance of different CNN architectures (ResNet and DenseNet), normalization methods, and slice selection techniques. The results demonstrate the potential of CNNs in predicting prostate cancer progression, with the best-performing model achieving an accuracy of 0.712. The study highlights the importance of appropriate image normalization and slice selection methods for optimal performance. The findings suggest that CNNs could serve as a valuable tool for aiding clinical decision-making in prostate cancer management.